Many different disciplines use motion analysis systems to capture movements and postures of the human body. To make realistic animations for movies and computer games, movements of the actor are captured and mapped on a character. In sports, motion analysis techniques are used to analyze and improve performances. In the field of medicine and rehabilitation, recordings of human motion can be used, for example, to evaluate gait patterns.
Motion capture is often performed using magnetic or camera-based systems. In camera-based systems, reflective or light-emitting markers attached to the body are observed by a number of cameras from which the 3D position can be reconstructed using triangulation of each camera 2D image. With magnetic trackers, magnetic sensors measure the field as emitted by a source placed near the subject from which position and orientation of the sensor with respect to the source can be calculated. The set-up of external emitters or cameras limits the working volume where the subject can be captured and impedes many applications. Besides this major limitation and high costs, optical systems suffer from occlusion and reflection problems and magnetic trackers are easily disturbed by metallic objects in the vicinity.
To capture human body movements and postures without the need for external emitters or cameras several other systems are available. Mechanical trackers utilize rigid or flexible goniometers which are worn by the user. These angle measuring devices provide joint angle data to kinematic algorithms which are used to determine body posture. Attachment of the body-based linkages as well as the positioning of the goniometers present several problems. The soft tissue of the body allows the position of the linkages relative to the body to change as motion occurs. Even without these changes, alignment of the goniometer with body joints is difficult. This is specifically true for multiple degree of freedom (DOF) joints, like the shoulder.
U.S. Pat. Nos. 6,820,025 to Bachmann et al. and 5,645,077 to Foxlin, as well as Luinge H. J., “Inertial Sensing of Human Movement,” Ph.D. thesis, University of Twente (2002), describe the use of miniature inertial and magnetic sensor modules to measure body segment orientation. These sensors measure the motion of the segment on which they are attached, independently of other system with respect to an earth-fixed reference system. They consist of gyroscopes, which measure angular velocities, accelerometers, which measure accelerations including gravity, and magnetometers measuring the earth magnetic field. When it is known to which body segment a sensor is attached, and when the orientation of the sensor with respect to the segments and joints is known, the orientation of the segments can be expressed in the global frame. By using the calculated orientations of individual body segments and the knowledge about the segment lengths, orientation between segments can be estimated and a position of the segments can be derived under strict assumptions of a linked kinematic chain (articulated model). This method is well-known in the art and assumes an articulated rigid body in which the joints only have rotational degrees of freedom, as described in Bachmann.
In such an approach of adding up vectors of different orientation, orientation errors, calibration errors and joint model errors accumulate as position errors in the connecting body parts. In fact, a human body and its joints can not be modeled as a pure kinematic chain with well-defined joints like hinge-joints and ball-and-socket-joints. Each human joint allows some laxity in all directions (both position and orientation) other than its main direction of movement. Further, to be able to track complex human joints and non-rigid body parts such as the back and shoulder accurately, more than three degrees of freedom, as given by an orientation measurement, are required. Furthermore, importantly, with only orientation driven motion capture, it is not possible to analyze the clearance of both feet, which occurs during running or jumping. Using this approach, it is also not possible to accurately determine the displacement of the body with respect to a coordinate system not fixed to the body.
The sensor modules should be attached tightly with respect to the bones. However, during use of the motion capture system, sensors can move with respect to the bone. This is caused by the elastic properties of the skin, fat and muscle contractions. These soft tissue artifacts are also present in other motion tracking systems and will introduce significant errors.